Safety Assessment of the JiaoLong Deep-sea Manned Submersible based on Bayesian Network

Abstract

Safety assessment is of great importance to the deep-sea manned submersible, but little literature has been reported on this topic. The goal of this paper is to work out an effective tool for the safety assessment of the deep-sea manned submersible according to the study of JiaoLong, which is the first manned submersible that can dive more than 7,000 meters in China. In this paper, a relatively new subsystem division of the manned submersible is introduced firstly. Furthermore, a BN-based safety assessment method is proposed which combines the Bayesian Network (BN) and data-driven fault detection algorithms. Based on the BN, qualitative and quantitative analysis can both be implemented. Moreover, real-time safety assessment can be realized by combining data-driven fault detection algorithms. The proposed method is verified on the JiaoLong manned submersible by constructing and analyzing the BN. Also, an example of the propeller fault detection using kernel principal component analysis (KPCA) is displayed to illustrate how to employ the proposed method in real-time.